Predictive part maintenance

ABSTRACT

The present disclosure provides for predictive part maintenance by generating a reliability curve for an aircraft based on historic removals; setting a removal threshold on the reliability curve; tracking an installation of a given instance of the aircraft part into a given aircraft; tracking a number of cycles of the given instance of the aircraft part based on operations of the given aircraft in which the given instance of the aircraft part is installed; and in response to the number of cycles of the given instance of the aircraft part satisfying the removal threshold, transmitting a service alert to an operator of the given aircraft.

FIELD

The present disclosure generally relates to systems and methods fordetermining reliability of various parts of a vehicle, such as anaircraft.

BACKGROUND

Vehicles are used to transport individuals between locations. Forexample, commercial aircraft are used to transport passengers betweenvarious locations. A typical aircraft includes thousands, if notmillions, of component parts. For example, each system, sub-system,structure, and the like of an aircraft may be formed from thousands ofcomponent parts.

As can be appreciated, aircraft operators and passengers value flightsthat are on time. Maintenance operations pose a potential delay foraircraft. For example, if it is determined that a particular part of anaircraft needs to be replaced between flights, a maintenance crewreplaces the part. However, such maintenance procedures may cause theensuing departure time to be delayed while the old part is replaced witha new part.

SUMMARY

The present disclosure provides a method in one aspect, the methodincluding: generating a reliability curve for a vehicle part by:collecting usage data regarding a plurality of instances of the vehiclepart, wherein for each respective instance of the vehicle part of theplurality of instances of the vehicle part: the usage data for therespective instance are collected at a time of removal from a vehicleassociated with the respective instance, the usage data indicate anumber of cycles accumulated by the respective instance of the vehiclepart at the time of removal from the associated vehicle, and the usagedata indicate whether the respective instance of the vehicle part is ina faulted state at the time of removal from the associated vehicle;determining a percentage of instances of the vehicle part that are inthe faulted state at a plurality of numbers of cycles; and fitting aprobability distribution function that defines the reliability curve tothe usage data based on the percentage of instances of the vehicle partthat are in the faulted state for each of the plurality of numbers ofcycles; setting a removal threshold on the reliability curve; trackingan installation of a given instance of the vehicle part into a givenvehicle; tracking a number of cycles of the given instance of thevehicle part based on operations of the given vehicle in which the giveninstance of the vehicle part is installed; and in response to the numberof cycles of the given instance of the vehicle part satisfying theremoval threshold, transmitting a service alert to an operator of thegiven vehicle.

In one aspect, in combination with any example method above or below,setting the removal threshold on the reliability curve furthercomprises: determining a number of historic removals of the vehicle partused to generate the reliability curve; and in response to the number ofhistoric removals being below a predetermined setpoint, setting theremoval threshold as a fixed number of cycles of the vehicle part.

In one aspect, in combination with any example method above or below,setting the removal threshold on the reliability curve furthercomprises: setting the removal threshold between a first point and asecond point on the reliability curve, wherein the first point indicatesa first number of cycles of the vehicle part at which at least a firstpercentage of operators historically removed the vehicle part fromvehicle, wherein the second point indicates a second number of cycles ofthe vehicle part that was previously set for the removal threshold forthe vehicle part, and setting a larger of the first point and the secondpoint as the removal threshold.

In one aspect, in combination with any example method above or below,the method further comprises: setting an reorder threshold based on: asafety stock level, the removal threshold; a lead time for the vehiclepart; a use rate of the given vehicle; and in response to the number ofcycles of the given instance of the vehicle part satisfying the reorderthreshold, transmitting an inventory alert to an operator of the givenvehicle.

In one aspect, in combination with any example method above or below,the method further comprises: determining an intersection of thereliability curve and the removal threshold; comparing the intersectionagainst an upper reliability threshold for the vehicle part; and inresponse to the number of cycles at the intersection being greater thanthe upper reliability threshold, transmitting a quality alert to anoperator of the given vehicle.

In one aspect, in combination with any example method above or below,the method further comprises: determining an intersection of thereliability curve and the removal threshold; comparing the intersectionagainst an a lower reliability threshold for the vehicle part; and inresponse to the number of cycles at the intersection being lower thanthe lower reliability threshold, transmitting a quality alert to anoperator of the given vehicle.

In one aspect, in combination with any example method above or below,the method further comprises: in response to receiving additionalsamples of removals of the vehicle part, re-setting the removalthreshold to a different value on the reliability curve.

In one aspect, in combination with any example method above or below,the given vehicle is an aircraft.

The present disclosure provides a method in one aspect, the methodincluding: generating a reliability curve for a vehicle part by:collecting usage data regarding a plurality of instances of the vehiclepart, wherein for each respective instance of the vehicle part of theplurality of instances of the vehicle part: the usage data for therespective instance are collected at a time of removal from a vehicleassociated with the respective instance, the usage data indicate anumber of flight hours accumulated by the respective instance of thevehicle part at the time of removal from the associated vehicle, and theusage data indicate whether the respective instance of the vehicle partis in a faulted state at the time of removal from the associatedvehicle; determining a percentage of instances of the vehicle part thatare in the faulted state at a plurality of numbers of flight hours; andfitting a probability distribution function that defines the reliabilitycurve to the usage data based on the percentage of instances of thevehicle part that are in the faulted state for each of the plurality ofnumbers of flight hours; setting a removal threshold on the reliabilitycurve; tracking an installation of a given instance of the vehicle partinto a given vehicle; tracking a number of flight hours of the giveninstance of the vehicle part based on operations of the given vehicle inwhich the given instance of the vehicle part is installed; and inresponse to the number of flight hours of the given instance of thevehicle part satisfying the removal threshold, transmitting a servicealert to an operator of the given vehicle.

In one aspect, in combination with any example method above or below,setting the removal threshold further comprises: determining a number ofhistoric removals of the vehicle part used to generate the reliabilitycurve; and in response to the number of historic removals being below apredetermined setpoint, setting the removal threshold as a fixed valueof flight hours for the vehicle part.

In one aspect, in combination with any example method above or below,setting the removal threshold further comprises: setting the removalthreshold between a first point and a second point on the reliabilitycurve, wherein the first point indicates a first number of flight hoursaccumulated by the vehicle part at which at least a first percentage ofoperators historically removed the vehicle part from vehicle, andwherein the second point indicates a second number of flight hoursaccumulated by the vehicle part that was previously set as the removalthreshold for the vehicle part, and wherein a larger of the first pointand the second point is set as the removal threshold.

In one aspect, in combination with any example method above or below,the method further comprises: setting an inventory threshold based on: asafety stock level; the removal threshold; a lead time for the vehiclepart; and a use rate of the given vehicle; and in response to the numberof flight hours accumulated by the given instance of the vehicle partsatisfying the inventory threshold, transmitting an inventory alert toan operator of the given vehicle.

In one aspect, in combination with any example method above or below,the method further comprises: determining an intersection of thereliability curve and the removal threshold; comparing the intersectionagainst an upper reliability threshold for the vehicle part; and inresponse to a number of cycles at the intersection being greater thanthe upper reliability threshold, transmitting a quality alert to anoperator of the given vehicle.

In one aspect, in combination with any example method above or below,the method further comprises: determining an intersection of thereliability curve and the removal threshold; comparing the intersectionagainst a lower reliability threshold for the vehicle part; and inresponse to a number of cycles at the intersection being lower than thelower reliability threshold, transmitting a quality alert to an operatorof the given vehicle.

In one aspect, in combination with any example method above or below,the method further comprises: in response to receiving additionalsamples of removals of the vehicle part, re-setting the removalthreshold to a different value on the reliability curve.

In one aspect, in combination with any example method above or below,the given vehicle is an aircraft.

The present disclosure provides a system in one aspect, the systemincluding: a processor; and a memory storage device, includinginstructions that when performed by the processor enable the processorto perform an operation comprising: generating a reliability curve for avehicle part; setting a removal threshold on the reliability curve;tracking an installation of a given instance of the vehicle part into agiven vehicle; tracking operations of the given instance of the vehiclepart based on operations of the given vehicle in which the giveninstance of the vehicle part is installed; and in response to theoperations of the given instance of the vehicle part satisfying theremoval threshold, transmitting a service alert to an operator of thegiven vehicle.

In one aspect, in combination with any example system above or below,setting the removal threshold further comprises: determining a number ofhistoric removals of the vehicle part used to generate the reliabilitycurve; and in response to the number of historic removals being below apredetermined setpoint, setting the removal threshold as a fixed valueof operations of the vehicle.

In one aspect, in combination with any example system above or belowsetting the removal threshold further comprises: determining a number ofhistoric removals of the vehicle part used to generate the reliabilitycurve; and in response to the number of historic removals exceeding apredetermined setpoint: setting the removal threshold between a firstpoint and a second point on the reliability curve, wherein the firstpoint indicates a first number of operations of the vehicle part atwhich at least a first percentage of operators historically removed thevehicle part from vehicle, and wherein the second point indicates asecond number of operations of the vehicle part at which at least asecond percentage of operators historically removed the vehicle partfrom the vehicle that is greater than the first percentage.

In one aspect, in combination with any example system above or below,generating the reliability curve for the vehicle part further comprises:collecting usage data from several instances of the vehicle part at atime of removal from an associated vehicle that indicate a number ofcycles accumulated by a particular instance of the vehicle part at thetime of removal from the associated vehicle and whether the particularinstance of the vehicle part is in a faulted state at the time ofremoval; determining percentages the vehicle part in a faulted state atper number of cycles; and fitting a probability distribution function tothe usage data based on the percentages of the vehicle part in thefaulted state.

In one aspect, in combination with any example system above or below,generating the reliability curve for the vehicle part further comprises:collecting usage data from several instances of the vehicle part at atime of removal from an associated vehicle that indicate a number offlight hours accumulated by a particular instance of the vehicle part atthe time of removal from the associated vehicle and whether theparticular instance of the vehicle part is in a faulted state at thetime of removal; determining percentages the vehicle part in a faultedstate per hours of flight; and fitting a probability distributionfunction to the usage data based on the percentages of the vehicle partin the faulted state.

In one aspect, in combination with any example system above or below,the operations of the vehicle part are tracked based on a number ofcycles of the vehicle part while the vehicle part is installed, furthercomprising: setting an inventory threshold based on: the removalthreshold; a lead time for the vehicle part; a use rate of the givenvehicle; and a cycles per flight coefficient for the vehicle part; andin response to the operations of the given instance of the vehicle partsatisfying the inventory threshold, transmitting an inventory alert toan operator of the given vehicle.

In one aspect, in combination with any example system above or below,the operations of the vehicle part are tracked based on a number offlight hours of the vehicle while the vehicle part is installed, furthercomprising: setting an inventory threshold based on:

the removal threshold; a lead time for the vehicle part; and a use rateof the given vehicle; and in response to the operations of the giveninstance of the vehicle part satisfying the inventory threshold,transmitting an inventory alert to an operator of the given vehicle.

In one aspect, in combination with any example system above or below,the operation further comprises: determining an intersection of thereliability curve and the removal threshold; comparing the intersectionagainst an upper reliability threshold and a lower reliability thresholdfor the vehicle part; and in response to a number of operations of thevehicle part at the intersection being one of greater than of the upperreliability threshold or less than the lower reliability threshold,transmitting a quality alert to an operator of the given vehicle.

In one aspect, in combination with any example system above or below,the given vehicle is an aircraft.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features can be understoodin detail, a more particular description, briefly summarized above, maybe had by reference to example aspects, some of which are illustrated inthe appended drawings.

FIG. 1 is a diagrammatic representation of a front perspective view of avehicle, according to aspects of the present disclosure.

FIG. 2 is a schematic representation of a part reliability determinationsystem, according to aspects of the present disclosure.

FIG. 3 is a flowchart of a method of determining part reliability,according to aspects of the present disclosure.

FIG. 4 is a diagrammatic representation of a graph of a reliabilitycurve and a Cumulative Distribution Function curve with variousthresholds defined thereon, according to aspects of the presentdisclosure.

FIG. 5 is a diagrammatic representation of an inventory level, accordingto aspects of the present disclosure.

FIG. 6 is a flowchart of a method for generating service and/orinventory alerts for aircraft parts based on aircraft operations,according to aspects of the present disclosure.

DETAILED DESCRIPTION

Aircraft operators routinely perform maintenance at scheduled intervals.Often, maintenance is scheduled such that aircraft are taken out ofservice at times that minimally disrupt scheduled flights, and atlocations where parts and technicians are available, while alsoextending the time between scheduled maintenance operations for as longas possible. Some parts of an aircraft are associated with sensors thatconstantly or periodically monitor one or more characteristics of thoseparts, but other parts (and some aspects of the monitored parts) aremonitored by technicians when the aircraft is on the ground. To speed upturn around for inspections or repair, and maximize the availability ofan aircraft for flight operations, some parts of an aircraft may bereplaced before a fault occurs in the part, and some parts may beremoved from the aircraft (and replaced with another part) to allow thepart to be inspected independently of the aircraft. Parts that areremoved and replaced for inspection may be installed in another aircraftor later reinstalled in the original aircraft once the inspection (or arepair) is complete. Accordingly, as an aircraft is used and maintenanceis performed, the use-status of the various parts of that aircraft willvary.

Various parts of an aircraft are designed to be consumed duringoperations (e.g., landing gear tires) and replaced at particularintervals, which are referred to herein as consumable parts. Incontrast, other parts are designed to be repaired if a non-conformancedevelops (e.g., a landing gear strut), and are referred to herein asnon-consumable or durable parts. Durable parts that can be removed andreplaced with another part for inspections or repair independently ofthe rest of the aircraft and are placed back into service independentlyof an individual aircraft's operations are referred to herein asroutable parts.

The present disclosure provides systems and methods for generatingalerts, regarding when an individual component approaches a servicelifetime threshold, and could be repaired or replaced. Such alerts maybe used by a wide variety aircraft operators, including airports,airlines, manufacturers, contracted maintenance entities, and agentsthereof. Although the examples given herein are presented primarilyusing aircraft, the present disclosure is equally applicable foroperators of other types of vehicles, such as, for example, trucks,cars, buses, spacecraft, watercraft, and trains. Accordingly, anyreference made to an aircraft may be understood to also apply to anothertype of vehicle, and any reference to time in flight, flight, oraircraft specific parts (e.g., a tail) may be understood to refer totime-in-travel, travel, or a part of a different type of vehicle (e.g.,a caboose, a stern, a rear bumper).

Generally, the time or cycles or days of operation of individual partsmay be tracked in relation with the aircraft in which the parts areinstalled. A service alert may be generated in anticipation of the partreaching a service lifetime threshold. An inventory alert may begenerated in conjunction with the service alert based on an expected userate so that new parts can be ordered and delivered (and technicians andflight plans scheduled) to a particular location in time to replace thatpart before the part is fully consumed or develops a nonconformance.

Aspects of the present disclosure may be understood in two stages. Afirst stage collects data for various parts installed on severalaircraft. The collected data indicate when an individual part wasinstalled on a particular aircraft, when (if) the part is removed fromthe aircraft, and whether the part is in a faulted or non-faulted statewhen removed. These data, in conjunction with operational data for theaircraft on which the parts are installed, are used to determinelifetime reliability curves for each type part that is tracked inaggregate. A second stage uses the lifetime curves to develop alertthresholds, and transmits alerts to responsible parties when theoperations of an aircraft affect an individual part.

Various aspects of the present disclosure provide systems and methodsthat are configured to determine life expectancy of various parts of avehicle, such as an aircraft. Knowledge of vehicle part life has variousadvantages, including an ability to provide improved vehicle design,efficient support of vehicle operation, the ability to predict componentremoval times, the ability to determine a number of spare parts to haveon hand, and support of maintenance checks.

Certain aspects of the present disclosure provide systems and methods ofdetermining reliability of components of a vehicle, such as an aircraft.The systems and methods determine part life distribution. After the partlife distribution is determined, the systems and methods are able todetermine reliability and remaining life of the various parts of thevehicle. For example, the systems and methods are able to determine howlong a part will last, an average life of a part, when a part may besusceptible to faults, risk associated with an existing part remainingon a vehicle, and a number of spare parts an aircraft operator needs fora predetermined time period.

Reliability in relation to a part relates to the probability that thepart will perform the intended function of that part for a stated periodof time. Some of the systems and methods described herein assess thereliability of one or more parts of a vehicle in order to predict whenthe part(s) should be replaced before a potential fault.

In at least one aspect, a part reliability determination system collectsand stores flight hours and flight cycles during which parts have beenin service. The flight hours and flight cycles are stored as usage datawithin a vehicle usage database. The part reliability determinationsystem may, for each part, store a time since the part was installed(TSI) or since the part was last overhauled (TSO) or since it was firsttime to use (TSN), and the number of cycles (in which a cycle ismeasured as a departure to arrival of a vehicle) since the part wasinstalled (CSI) or since the part was last overhauled (CSO) or since itwas first time to use (TSN). However, TSI/CSI, TSO/CSO and TSN/CSN maynot be directly available in part removal data. For example, operatorsmay not be required to provide TSI/CSI when a part is removed from anaircraft or due to time constraints, or operators may not have time tocollect the data. Accordingly, various aspects of the present disclosureprovide a part reliability determination system that is configured todetermine TSI/CSI, TSO/CSO, and TSN/CSN for removed parts.

In at least one aspect, the part reliability determination system isalso configured to determine TSI and CSI for parts that are currently ona vehicle, such as an aircraft. In order to determine the TSI and CSIdata, the part reliability determination system retrieves vehicleconfiguration data and vehicle readiness log data, which may be storedin a vehicle usage database. By analyzing the vehicle configuration dataand vehicle readiness log data and component removals, the partreliability determination system determines how many of parts with thesame part numbers have been removed and how many of them are still onthe vehicle. By determining part removals, the part reliabilitydetermination system determines when a part was installed and how longthe component has been on the vehicle.

Aspects of the present disclosure provide systems and methods thatintegrate data sources and estimate TSI/CSI from both removed parts andparts that are still on aircrafts. The systems and methods areconfigured to predict when parts of a vehicle are susceptible to faultbased on aircraft part life distribution. By predicting when a part issusceptible to fault, the systems and methods allow for the part to bereplaced before such predicted time.

FIG. 1 is a diagrammatic representation of a front perspective view of avehicle, in this example an aircraft 10, according to an exemplaryaspect of the present disclosure. The aircraft 10 includes a propulsionsystem 12 that may include two turbofan engines 14, for example.Optionally, the propulsion system 12 may include more engines 14 thanshown. The engines 14 are carried by wings 16 of the aircraft 10. Inother aspects, the engines 14 may be carried by a fuselage 18 and/or anempennage 20. The empennage 20 may also support horizontal stabilizers22 and a vertical stabilizer 24.

The fuselage 18 of the aircraft 10 defines an interior cabin, which mayinclude a cockpit, one or more work sections (for example, galleys,personnel carry-on baggage areas, and the like), one or more passengersections (for example, first class, business class, and economysections), and an aft section. Each of the sections may be separated bya cabin transition area, which may include one or more class dividerassemblies. Overhead stowage bin assemblies may be positioned throughoutthe interior cabin.

The aircraft 10 includes numerous systems and sub-systems that includenumerous parts. For example, the propulsion system 12 includes thousandsof component parts. As another example, each lavatory onboard theaircraft 10 includes thousands of component parts. The entire aircraft10 includes millions of separate and distinct parts that together formthe aircraft 10. A part reliability determination system is used todetermine a life distribution for each of the parts, and predict aremaining life (for example, time until the part may be susceptible tofault) for the parts of the aircraft 10. In at least one aspect, thepart reliability determination system determines the remaining life inrelation to flight hours and/or flight cycles of the aircraft 10.

Alternatively, instead of an aircraft, aspects of the present disclosuremay be used with various other vehicles, such as automobiles, buses,locomotives and train cars, watercraft, spacecraft, and the like.

FIG. 2 is a schematic representation of a part reliability determinationsystem 100, according to an exemplary aspect of the present disclosure.The part reliability determination system 100 includes a parts database102 that is in communication with a part life distribution control unit104, and a part life prediction control unit 106, such as through one ormore wired or wireless connections. The part life distribution controlunit 104 and the part life prediction control unit 106 are also incommunication with one another, such as through one or more wired orwireless connections.

The part reliability determination system 100 may be onboard theaircraft 10 (shown in FIG. 1) or remotely located from the aircraft 10,such as at a land-based monitoring station. In at least one aspect, oneor both of the part life distribution control unit 104 or the part lifeprediction control unit 106 may be onboard the aircraft 10, while theparts database 102 is remotely located therefrom, such as at aland-based monitoring station. In at least one aspect, the parts lifedistribution control unit 104 and the part life prediction control unit106 may be in communication with the parts database 102 through variouscommunication networks, such as, but not limited to, the Internet.

As shown, the part life distribution control unit 104 and the part lifeprediction control unit 106 may be separate and distinct control units.Optionally, the part life distribution control unit 104 and the partlife prediction control unit 106 may be components of a single controlunit or processing system.

In at least one aspect, the parts database 102 includes a historic partstorage unit 110, and a current part storage unit 112. The historic partstorage unit 110 stores data for all type of parts of the aircraftcompiled over time for numerous aircraft. For example, the historic partstorage unit 110 may store data regarding the useful life of all partscurrently on an aircraft but aggregated for past uses with respect tothe aircraft and various other aircraft (for example, other same-moldedaircraft). The data may include life data (that is, the time of actualusage) for each of the parts. In at least one aspect, the historic partstorage unit 110 stores all available data for the parts currently onthe aircraft, but which have been used with respect to the currentaircraft and all other aircraft for which such data is available.

As an example, a particular aircraft includes a particular part. Thehistoric part storage unit 110 stores available data collected from theuse of different parts of the same type of the installed particularpart. These data include the aggregated lifetimes and fault statuses forthe parts of the particular type, which have been previously used withrespect to several aircraft (potentially including the particularaircraft). As such, the historic part storage unit 110 may store datafor a particular type of part used in connection with hundreds,thousands, or more aircraft.

The current part storage unit 112 stores current life data for all partscurrently on the aircraft. For example, the current part storage unit112 stores data regarding time since each part was installed (TSI),cycles since each part was installed (CSI), and the like. In thismanner, the current part storage unit 112 stores current life dataregarding the actual usage (that is, life) of each part.

In at least one aspect, the part reliability determination system 100also includes a vehicle usage database 114 that stores vehicle usagedata indicative of the actual usage of a vehicle having the parts. Thevehicle usage database 114 is in communication with the part lifeprediction control unit 106, such as through one or more wired orwireless connections. As an example, the vehicle usage database 114stores vehicle usage data for an aircraft, such as in terms of flighthours (that is, actual hours of in-flight operation) and flight cycles(that is, total number of cycles, in which a cycle is defined as adeparture and associated arrival).

The part life prediction control unit 106 is configured to determine aremaining life of a part of a vehicle based on a part life distributionof the particular type of part. As such, the part life predictioncontrol unit 106 is configured to determine the remaining life of thepart based on current life data regarding the part (such as stored inthe current part storage unit 112) and the part life distributionrelated to the part, as determined by the part life distribution controlunit 104. In at least one aspect, the part life prediction control unit106 determines the remaining life of a part as a function of thedetermined part life distribution of the particular type of part and theactual usage of the vehicle. For example, the part life predictioncontrol unit 106 may determine a remaining life of a current part of anaircraft in terms of flight hours and/or flight cycles of an aircraft.

In operation, the part life distribution control unit 104 analyzes thehistoric part data for each part stored in the historic part storageunit 110. The part life distribution control unit 104 determines a lifedistribution for a particular part based on the historic part data,which, as noted, may be compiled with respect to uses in relation toseveral aircraft in a fleet of aircraft (owned or operated by one entityor distributed across several entities). In at least one aspect, thepart life distribution control unit 104 determines a life distributionfor a particular type of part based on analysis of the historic partdata. In at least one aspect, the part life distribution control unit104 may determine the life distribution based on one or moremathematical models and formulas.

As but one example, the part life distribution control unit 104 maydetermine an average life of a particular type of part based onhistorical data regarding hundreds, thousands, or millions of actuallives (that is, times of actual use of a particular part) of theparticular type of part. For example, the part life distribution controlunit 104 may be based on hundreds, thousands, or even millions of actuallife usages of prior parts. As such, the part life distribution controlunit 104 determines a part life distribution for a particular type ofpart. In at least one aspect, the part life distribution control unit104 determines a part life distribution for each particular type of partof an aircraft. In at least one other aspect, the part life distributioncontrol unit 104 determines a part life distribution for less than eachparticular type of part of an aircraft.

After determining a part life distribution for a particular type of partof an aircraft, the part life prediction control unit 106 analyzes thecurrent part data of the aircraft, which is stored in the current partstorage unit 112. The part life prediction control unit 106 assesses thevarious current parts (that is, the parts that currently form one ormore portions) of the aircraft, as stored in the current part data. Forexample, the current part data stores the TSI, CSI, and/or the like forthe current parts. The TSI and CSI for each part may be provided andstored in the current part storage unit 112. In at least one otheraspect, the TSI and CSI may not be provided. Instead, the part lifeprediction control unit 106 may determine the TSI and CSI based on datesof prior part removals, statistical analysis of installation and removaldates of parts, and/or the like. The part life prediction control unit106 analyzes the part life distribution for each type of part (asdetermined by the part life distribution control unit 104) in relationto each current part of the aircraft to predict a remaining life of thecurrent parts.

In at least one aspect, the part life prediction control unit 106determines a current life of a part based on vehicle usage data storedin the vehicle usage database 114. For example, the part life predictioncontrol unit 106 correlates the TSI and/or CSI of a part andcross-references such current life data with vehicle usage data storedin the vehicle usage database 114. Based on the TSI and/or CSI and theactual vehicle usage (in terms of hours and/or cycles), the part lifeprediction control unit 106 determines a current life of a part in termsof vehicle hours of usage and/or cycles. The part life predictioncontrol unit 106 may then compare the current life of the part inrelation to the part life distribution for that particular type of partto predict a remaining life of the part, such as in terms of remaininghours and/or cycles.

As an example, a vacuum generator for a lavatory onboard the aircraft isone type of part. The part life distribution control unit 104 analyzesthe historic data for vacuum generators that is stored in the historicpart storage unit 110. Based on the historic data for vacuum generators,the part life distribution control unit 104 determines a part lifedistribution for vacuum generators. For example, the part lifedistribution control unit 104 may determine an average useful life for avacuum generator based on the historic data for vacuum generators storedin the historic part storage unit 110. The part life prediction controlunit 106 then analyzes the current part storage unit 112 and confirmsthat a vacuum generator is onboard the aircraft. The current partstorage unit 112 stores current data for the vacuum generator. Thecurrent data includes the TSI, for example, for the vacuum generatoronboard the aircraft. The part life prediction control unit 106 thencorrelates the part life distribution for vacuum generators (asdetermined by the part life distribution control unit 104) with thecurrent life data for the vacuum generator onboard the aircraft. Basedon the correlation between the part life distribution for vacuumgenerators and the current data for the actual vacuum generator onboardthe aircraft, the part life prediction control unit 106 predicts aremaining useful life for the actual vacuum generator. As an example,the part life prediction control unit 106 subtracts the time of use ofthe actual vacuum generator (for example, flight hours elapsed from TSIuntil current date) from the part life distribution for vacuumgenerators. In this manner, the part life prediction control unit 106predicts when the actual vacuum generator may be susceptible to fault.In various aspects, the part life prediction control unit 106 outputs aprediction signal or alert to a flight operation, maintenance crew,automated inventory/maintenance scheduler, or the like, when the TSI orCSI crosses a predetermined threshold for ongoing reliability.

As another example, a pump for a bilge onboard a ship is one type ofpart. The part life distribution control unit 104 analyzes the historicdata for bilge pumps that is stored in the historic part storage unit110. Based on the historic data for bilge pumps, the part lifedistribution control unit 104 determines a part life distribution forbilge pumps. For example, the part life distribution control unit 104may determine an average useful life for a bilge pump based on thehistoric data for bilge pumps stored in the historic part storage unit110. The part life prediction control unit 106 then analyzes the currentpart storage unit 112 and confirms that a bilge pump is onboard theship. The current part storage unit 112 stores current data for thebilge pump. The current data includes the TSI, for example, for thebilge pump onboard the ship. The part life prediction control unit 106then correlates the part life distribution for bilge pumps (asdetermined by the part life distribution control unit 104) with thecurrent life data for the bilge pump onboard the ship. Based on thecorrelation between the part life distribution for bilge pumps and thecurrent data for the actual bilge pump onboard the ship, the part lifeprediction control unit 106 predicts a remaining useful life for theactual bilge pump. As an example, the part life prediction control unit106 subtracts the time of use of the actual bilge pump (for example,hours at sea elapsed from TSI until current date) from the part lifedistribution for bilge pumps. In this manner, the part life predictioncontrol unit 106 predicts when the actual bilge pump may be susceptibleto fault. In various aspects, the part life prediction control unit 106outputs a prediction signal or alert to a voyage operation, maintenancecrew, automated inventory/maintenance scheduler, or the like, when theTSI or CSI crosses a predetermined threshold for ongoing reliability.

The part reliability determination system 100 operates in such a mannerfor at least one part of the aircraft. In at least one aspect, the partreliability determination system 100 determines a part life distributionand predicts a remaining useful life for each and every part of theaircraft (or any subset thereof).

As used herein, the term “control unit,” “central processing unit,”“unit,” “CPU,” “computer,” or the like may include any processor-basedor microprocessor-based system including systems using microcontrollers,reduced instruction set computers (RISC), application specificintegrated circuits (ASICs), logic circuits, and any other circuit orprocessor including hardware, software, or a combination thereof capableof executing the functions described herein. Such are exemplary only,and are thus not intended to limit in any way the definition and/ormeaning of such terms. For example, the part life distribution controlunit 104 and the part life prediction control unit 106 may be or includeone or more processors that are configured to control operation of thepart reliability determination system 100, as described herein. The partlife distribution control unit 104 and the part life prediction controlunit 106 may be separate and distinct control units, or may be part ofthe same control unit.

The part life distribution control unit 104 and the part life predictioncontrol unit 106 are configured to execute a set of instructions thatare stored in one or more data storage units or elements (such as one ormore memories), in order to process data. For example, the part lifedistribution control unit 104 and the part life prediction control unit106 may include or be coupled to one or more memories. The data storageunits may also store data or other information as desired or needed. Thedata storage units may be in the form of an information source or aphysical memory element within a processing machine.

The set of instructions may include various commands that instruct thepart life distribution control unit 104 and the part life predictioncontrol unit 106 as processing machines to perform specific operationssuch as the methods and processes of the various aspects of the subjectmatter described herein. The set of instructions may be in the form of asoftware program. The software may be in various forms such as systemsoftware or application software. Further, the software may be in theform of a collection of separate programs, a program subset within alarger program or a portion of a program. The software may also includemodular programming in the form of object-oriented programming. Theprocessing of input data by the processing machine may be in response touser commands, or in response to results of previous processing, or inresponse to a request made by another processing machine.

The diagrams of aspects herein may illustrate one or more control orprocessing units, such as the part life distribution control unit 104and the part life prediction control unit 106. It is to be understoodthat the processing or control units may represent circuits, circuitry,or portions thereof that may be implemented as hardware with associatedinstructions (e.g., software stored on a tangible and non-transitorycomputer readable storage medium, such as a computer hard drive, ROM,RAM, or the like) that perform the operations described herein. Thehardware may include state machine circuitry hardwired to perform thefunctions described herein. Optionally, the hardware may includeelectronic circuits that include and/or are connected to one or morelogic-based devices, such as microprocessors, processors, controllers,or the like. Optionally, the part life distribution control unit 104 andthe part life prediction control unit 106 may represent processingcircuitry such as one or more of a field programmable gate array (FPGA),application specific integrated circuit (ASIC), microprocessor(s),and/or the like. The circuits in various aspects may be configured toexecute one or more algorithms to perform functions described herein.The one or more algorithms may include aspects of aspects disclosedherein, whether or not expressly identified in a flowchart or a method.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in a data storage unit (forexample, one or more memories) for execution by a computer, includingRAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatileRAM (NVRAIVI) memory. The above data storage unit types are exemplaryonly, and are thus not limiting as to the types of memory usable forstorage of a computer program.

FIG. 3 is a flowchart of a method of determining part reliability,according to an exemplary aspect of the present disclosure. Referring toFIGS. 2 and 3, at 200, historical data for a particular type of partused with one or more vehicles is stored, such as in the historic partstorage unit 110 of the parts database 102. At 202, current part datafor a current (that is, currently forming a portion) of a vehicle isstored, such as in the current part storage unit 112 of the partsdatabase 102.

A 204, a part life distribution for the particular type of part isdetermined, such as by the part life distribution control unit 104. At206, the current part data of the vehicle is analyzed, such as by thepart life prediction control unit 106. At 208, it is determined whetherthe vehicle includes the particular type of part. If the vehicle doesnot include the particular type of part, the method returns to 206 (oralternatively, ends). If, however, the vehicle does include theparticular type of part at 208, the method proceeds to 210, at which aremaining useful life of the current part is determined by the part lifeprediction control unit 106. For example, the part life predictioncontrol unit 106 analyzes the current part data (which is indicative ofa current lifetime of the current part) in relation to the part lifedistribution data for the particular type of part to predict a remaininguseful life of the current part.

At 212, the part life prediction control unit 106 outputs the remaininguseful life of the current part as a remaining useful life signal. Theremaining useful life signal may include one or more of a graphical,video, text, and/or audio signal that communicates the remaining usefullife of the current part to an individual.

In at least one aspect, the remaining useful life signal may becommunicated based on predetermined thresholds. For example, the partlife prediction control unit 106 may only output the remaining usefullife signal when the remaining useful life of the current part is withina predetermined amount of time and/or a predetermined number of cycles(such as a percentage thereof) until being susceptible to fault. In thismanner, the part life prediction control unit 106 is able to proactivelyalert individuals (such as aircraft operators, maintenance personnel,and the like) to required replacements. The method shown in FIG. 3 maybe performed for each and every part of a vehicle (or any subsetthereof). The predetermined threshold is chosen on the reliability curve300 (or the CDF curve 302), as discussed in greater detail in regard toFIG. 4.

Referring again to FIG. 2, in order to determine a current lifetime of apart, the part life prediction control unit 106 may first determine TSIand/or CSI for the part. The installation date for a particular part, aswell as a removal data for an immediately preceding part that wasreplaced by the particular part are stored in the current part storageunit 112. However, data regarding a particular installation or removalmay not be readily available. As described below, the part reliabilitydetermination system 100 may be configured to determine installation andremoval dates, even when such information is not initially reported orotherwise provided by an aircraft operator, manufacturer, or the like.After TSI and CSI data is determined, the part life prediction controlunit 106 may use one or more mathematical models (for example, a Weibullmodel, an exponential model, a gamma model, or a log normal model) topredict a remaining life of a part.

The parts life prediction control unit 106 integrates part removals thatare known (such as reported and stored in the historic part storage unit110), aircraft configuration data (such as indicating the various partsof the aircraft), part interchangeability data, and aircraft usage data(for example, flight hours and/or flight cycles), which may be stored inmemory and/or at a central monitoring station.

In at least one aspect, in order to determine remaining part life, thepart life prediction control unit 106 retrieves data regarding when parttracking first occurred. Such data may be stored in the historic partstorage unit 110 and/or the current part storage unit 112.

In at least one aspect, the part life prediction control unit 106 buildsa chain that links installations and removals of a particular type ofpart. In doing so, the part life prediction control unit 106 determinesan overall usage of the part. For example, if the particular type ofpart was removed and installed five times over a particular time period,the part life prediction control unit 106 is able to determine anaverage useful life of the part via the number of faulted removals overthe particular time period.

In some situations, the part life prediction control unit 106 may not beable to access reported dates of removal and installation. As such, thepart life prediction control unit 106 may be unable to construct a chainthat links installations and removals.

In at least one aspect, when the quantity of the part per aircraft (QPA)equals 1 (that is, there is only one of such part on the aircraft), partusage may be determined from the date of last removal. For example,based on the date the particular part was last removed, the part lifeprediction control unit 106 may determine a remaining useful life of thecurrent part (which replaced the removed part) based on the previousremoval date, as well as the part life distribution of the part, asdetermined by the part life distribution control unit 104.

If the quantity of part per aircraft (QPA) for the particular type ofpart is greater than 1 (that is, there are more than one of such part onthe aircraft), and there are no observed removals (for example, nostored dates of removal in the parts database 102), the part lifeprediction control unit 106 may determine each last removal of a partnumber with QPA>1 and may determine the usage from one last removal tothe end of a study for each part with the same part number. For example,the part life prediction control unit 106 may determine the last threeknown removals of a particular type of part. The part life predictioncontrol unit 106 may also determine usage of the parts from the time orcycle of the most recent removal to an end of a relevant study period.

In at least one aspect, the part life prediction control unit 106determines a number (n) of removals and QPA (m>1) of a part number. Insuch an m>n case, the part life prediction control unit 106 determinesthe usage of parts for m-n parts. In at least one aspect, the part lifeprediction control unit 106 determines which parts are flight hoursassociated and which parts are flight cycles associated, and uses flighthours to estimate part life if flight hours related, and flight cyclesto estimate part life if flight cycles associated.

The current part data stored in the current part storage unit 112 mayinclude one or both of TSI and CSI for a current part, as reported by amanufacturer or aircraft operator, for example. That is, a known firstdate and/or known first cycle of use of the part may be input into thecurrent part storage unit 112.

The part life distribution control unit 104 and/or the part lifeprediction control unit 106 detects vehicle configuration and quantity(for example, QPA) for a part from vehicle engineering configurationdata, aircraft readiness log data, IPC (illustrated part catalog),and/or the like, which may be stored in the current part storage unit112 and/or the vehicle usage database 114.

For a situation in which there is only one type of particular partonboard the aircraft (such as an aircraft including a single vacuumgenerator), such that the QPA=1, the part life prediction control unit106 is able to determine when the part is removed. In at least oneaspect, the part life prediction control unit 106 detects a removalsequence of parts with the same part number on an aircraft.

The part life prediction control unit 106 may determine a TSI or a CSIfor a part based, at least in part, on the part number or serial numberof the part. In at least one aspect, the part life prediction controlunit 106 is configured to detect when the part number began to betracked. For example, the date of first tracking of the part may be aportion of the current part data stored in the current part storage unit112. Further, the part life prediction control unit 106 analyzes theflight hours/cycles of the aircraft, such as stored in the vehicle usagedatabase 114. Accordingly, the part life prediction control unit 106 maythen detect the flight hours/cycles at the time when a part was removedfrom the aircraft. The part life prediction control unit then calculatesTSI and/or CSI for the removed part. The part life prediction controlunit 106 then identifies when a part that replaced (that is, thereplacement part) the removed part is installed on the aircraft. Thepart life prediction control unit 106 then monitors the flight times andcycles for the replacement part.

In at least one aspect, the part life prediction control unit 106detects all other parts of the aircraft with QPA=1 that have not beenreported as removed, but which there have been reported removal of thesame part number from a different aircraft. The part life distributioncontrol unit 104 and/or the part life prediction control unit 106 maythen detect how many other aircraft have the same part number.

In at least one aspect, the part reliability determination system 100(such as through one or both of the part life distribution control unit104 and/or the part life prediction control unit 106) detects whenremovals for the part number initially started being reported. Theflight hours/cycles of each aircraft are then determined for the timeswhen part removals first started being reported.

If QPA for a part onboard an aircraft exceeds 1, the part lifeprediction control unit 106 constructs a chain of removed and installedparts, such as in conjunction with their serial numbers. The part lifeprediction control unit 106 may identify a part in the chain. Next, itis determined when the part was installed on the aircraft and when thepart was removed from the aircraft. Such information may be stored inthe historic part storage unit 110. Next, the flight hours/cycles whenthe part was installed and the flight hours/cycles when the part wasremoved are stored in the historic part storage unit 110. Then, the partlife prediction control unit 106 calculates one or both of TSI and/orCSI of the part. The part life prediction control unit 106 identifiesthe installation date and the removal date for a removed part, both ofwhich may be stored in the historic part storage unit 110 and/or thecurrent part storage unit 112. Again, the part life prediction controlunit 106 may then identify the flight hours and/or cycles of theaircraft at the time when the part was installed and flight hours and/orcycles of the aircraft at the time when the part was removed.

In at least one aspect, the part reliability determination system 100 isconfigured to identify removed parts with blank serial numbers and/orremoved parts without installed part information. The part lifedistribution control unit 104 and/or the part life prediction controlunit 106 first identifies all removed parts with the same part number.Then, the first removal(s) of parts with the same part number areidentified. Next, dates when removals were first reported for the partnumber are identified. Next, the later of the aircraft delivery date(that is, when first delivered to an aircraft operator) and/or the datewhen removals were first reported is then identified. Then, the flighthours/cycles of the aircraft at the date that is later are identified.For example, the aircraft may have been delivered on Date 1, whileremoval of particular parts was reported on Date 2, which is afterDate 1. Thus, the date when removals were first reported (that is, Date2) is later than the date when the aircraft was delivered (that is, Date1). The flight hours/cycles of the aircraft are then identified fromDate 2. After the flight hours/cycles of the aircraft at the date thatis later are identified, a part removal data is identified.

The part reliability determination system 100 may also identify the lastremoval(s) with the same part numbers. The date(s) of removal(s) iscorrelated with the flight hours and/or cycles of the aircraft. The TSIand/or CSI is then calculated for the parts that are still on theaircraft.

The system 100 may also identify all removals with the same part numberand count the number (m) of removals for the parts. The system 100identifies QPA (n) for the parts. The system 100 then detects parts withm>n. The system 100 then determines the later of the delivery date ofthe aircraft and the date when removals started being reported. Thesystem 100 then calculates TSI and/or CSI for the part (in which m>n).

In at least one aspect, the part life prediction control unit 106calculates mean and standard deviation of TSI and mean and standarddeviation of CSI for each part number. The part life prediction controlunit 106 compares a normalized standard deviation of TSI and CSIseparately, and determines if the part is associated with flight hoursor flight cycles.

As indicated, the part life prediction control unit 106 may use one ormore mathematical models to determine a remaining life of a part. Forexample, the part life prediction control unit 106 may determine aremaining life of a part through use of a Weibull model, as shown below:

${pdf} = {{f(t)} = \left\{ \begin{matrix}{\frac{\beta}{\alpha}\left( \frac{t}{\alpha} \right)\left( {\beta - 1} \right)} & {{e^{- \frac{t^{\beta}}{\alpha}}t} \geq 0} \\0 & {t < 0}\end{matrix} \right.}$

where pdf is a probability distribution function, α is a scale parameterthat may be defined by one or both of a TSI or CSI for the part, and βis a shape parameter that may be defined by one of both of the TSI orCSI for the part. When β=1, the Weibull model becomes an exponentialmodel. Although the models and formulas presented herein are given inrelation to time t for a TSI example, it will be appreciated that a CSIexample may use the same formulas with the substitution of cycles c fortime t.

A time dependent fault rate, h, is given by

${h = {\frac{\beta}{\alpha}\left( \frac{t}{\alpha} \right)^{({\beta - 1})}}}.$

A cumulative distribution function (CDF), F(t), is given by

${F(t)} = {{1 - {e^{- {(\frac{t}{\alpha})}^{\beta}}t}} \geq {0.}}$

A Reliability function, R(t), is given by

${R(t)} = {e^{- {(\frac{t}{a})}^{\beta}}.}$

α is a scale parameter. β is a shape parameter. When β=1, the Weibullmodel is the exponential model.

FIG. 4 is a diagrammatic representation of a graph of a reliabilitycurve 300 and a CDF curve 302, according to an exemplary aspect of thepresent disclosure. The part life prediction control unit 106 determinesthe curves 300 and 302 via a mathematical model, such as the Weibullmodel. As shown, the curves are plotted in relation to flight hours 304and reliability probability 306, although in other aspects, the curvesmay be plotted in relation to cycles and reliability probability 306. Asshown in FIG. 4, the part life prediction control unit 106 determinesthat a particular part has served 90% of life at 6150 flight hours atpoint A, based on the CDF curve 302. Similarly, the part life predictioncontrol unit 106 determines that the particular part has a 10%reliability probability at point B. Based on one or both of the curves300 and 302, an operator may decide to replace the part at a particulartime, depending on a predetermined probability that the part willcontinue to be reliable.

As another example, the part life prediction control unit 106 maydetermine a remaining life of a part through use of the exponentialmodel, as shown below:

${{pdf}\mspace{14mu} {f(t)}} = \left\{ \begin{matrix}{\lambda*e^{{- \lambda}\; t}} & {t \geq 0} \\0 & {t < 0}\end{matrix} \right.$

in which the fault rate, h=λ is a constant fault rate.

Cumulative  Distribution  Function  (CDF):${F(t)} = \left\{ \begin{matrix}{1 - e^{{- \lambda}\; t}} & {t \geq 0} \\0 & {t < 0}\end{matrix} \right.$

Further, a survival function/reliability function, R(t), is given asR(t)=e^(−λt)

As another example, the part life prediction control unit 106 maydetermine a remaining life of a part through use of a gamma model, asshown below.

${{pdf}\text{:}\mspace{14mu} {f(t)}} = \frac{\beta^{\alpha}r^{\alpha - 1}e^{{- t}\; \beta}}{\Gamma (\alpha)}$${Fault}\mspace{14mu} {Rate}\text{:}\mspace{14mu} \frac{f(r)}{R(t)}$CDF:  F(T) = ∫₀^(T)f(t), dtReliability, R(t)  is  given  as  R(t) = 1 − F(t)

As another example, the part life prediction control unit 106 maydetermine a remaining life of a part through use of a log normal model,as shown below.

${{{pdf}\text{:}\mspace{14mu} {f(t)}} = {\frac{1}{\sigma \; t\sqrt{2\pi}}{e^{- \frac{1}{2\sigma^{2}}}\left( {{\ln (t)} - {\ln \left( T_{50} \right)}} \right)}^{2}}},{{{where}\mspace{14mu} T_{50}} = e^{\mu}}$${Fault}\mspace{14mu} {Rate}\text{:}\mspace{14mu} \frac{f(r)}{R(t)}$${{{CDF}\text{:}\mspace{14mu} {F(T)}} = {\int_{0}^{T}{\frac{1}{\sigma \; t\sqrt{2\pi}}{e^{- \frac{1}{2\sigma^{2}}}\left( {{\ln (t)} - {\ln \left( T_{50} \right)}} \right)}^{2}}}},{dt}$Reliability:  R(t) = 1 − F(t)

The part life prediction control unit 106 may determine remaining lifeof a part through one or more of the mathematic models shown above.Optionally, the part life prediction control unit 106 may determineremaining life of a part through various other mathematical models,formulas, and the like that fit a probability distribution function tothe usage data based on the percentages of the part in the faultedstate. For example, the part life prediction control unit 106 maydetermine remaining life through an average, mean, statisticalparameter(s), or the like of a particular type of part, as determinedthrough data compiled for thousands, if not millions, of like parts.

As described herein, aspects of the present disclosure provide partreliability determination systems and methods that may integrate datasources and estimate TSI and/or CSI, TSO and/or CSO, TSN and/or CSN fromboth removed parts and parts that are still on aircraft. Certain partsof an aircraft may be associated exclusively with one of flight hours orflight cycles. For example, landing gear parts are typically associatedwith cycles instead of hours. As such, part life distributions andpredictions for parts associated with cycles are determined in relationto cycles, and not flight hours. Conversely, part life distributions andpredictions for parts associated with flight hours are determined inrelation to hours, and not flight cycles. Some parts may be associatedwith both flight hours and flight cycles, and will be associated withtwo part life distributions: one based on flight hours and one based onflight cycles. Alerts for parts using two life distribution curves maybe generated for each curve, the earlier point on the two curves, thelater point on the two curves, or an average point from the two curves.For example, a hose component may have a service lifetime of “20,000flight hours or 5,000 cycles; whichever comes first”.

The data used to determine the reliability curves 300 and CDF curves 302for the various parts of the aircraft are collected from across severalaircraft. For example, all aircraft that include a given type of partmay provide usage data (e.g., time in flight, number of flights) andfault/replacement data on those parts that are aggregated to develop themodels that produce the curves. As more data are collected, the modelsand resulting curves can be made more accurate in determining theexpected lifetime of a given part.

However, some parts may have less data available than other parts. Forexample, a newly designed part (e.g., an update or upgrade to anexisting part) may not have any data available related to removals orfaults for several months or years until the first removal occurs. Asremovals occur (e.g., due to preventative maintenance or in response tofaults) more data are gathered for the real-world fault rate for thatpart, and the model for that part becomes more accurate in fitting theremoval and faulted states to a probability distribution function.However, in some aspects, operators may desired alerts to schedule theremoval of parts for which historical data are not yet available. Inother aspects, operators may desire to life-test a part with an unknownexpected life curve (or a removal setpoint believed to be too early inthe life cycle of the part) so that a given instance of the part remainsinstalled until a fault occurs (thus generating fault status data forthe part that indicate when a faulted state occurred).

A target removal threshold 310, an active removal threshold, and aforecasted replacement threshold 330 are illustrated on the CDF curve302 and the reliability curve 300.

The removal thresholds indicate the number of flight hours or cyclessince installation at which a given percent of a type of part hashistorically been removed from the aircraft of a fleet, and may bedetermined via a lookup function L(x), where x is a percentage, and L(x)returns the number of hours or cycles at which x % of the parts havebeen historically removed from an aircraft—either due to planned orunplanned maintenance events.

The target removal threshold 310 defines a point in operations for atype of aircraft part (e.g., a number of cycles or accumulated flighthours) at which G % of a type of part have historically been removedfrom aircraft—either due to planned or unplanned maintenance events. Thevalue G used to determine the target removal threshold 310 is a constant(e.g., 95%, 80%), but the resulting target removal threshold 310 mayvary in the number of flight hours since installation (TSI) or cyclessince installation (CSI) as additional data are collected on the removalof parts. For example, during a first year, 95% of a given type of partmay be removed from aircrafts after 2,000 hours of service, while duringa third year, 95% of that same part may be removed from aircrafts after3,000 hours of service, as operators become more familiar with thedurability of the part. Individual operators may select different valuesto use for G for individual parts. For example, a first operator mayselect G=95 for vacuum generators and G=90 for light fixtures, while asecond operator may select G=85 for vacuum generators and for lightfixtures.

The survival threshold 320 defines a point in operations for a part atwhich J % of the given type of part have historically survived past. Apart may be removed early in a lifetime due to issues during shakedown,improper installation, an overabundance of caution from maintenancepersonnel, a lack of training in maintenance personnel, etc. Forexample, during a first year, 20% of a given type of part may be removedfrom aircrafts after more than 200 hours of service, while during athird year, 95% of that same part may be removed from aircrafts after300 hours of service, as operators become more familiar with thedurability of the part. Individual operators may select different valuesto use for J for individual parts. For example, a first operator mayselect J=20 for vacuum generators and J=10 for light fixtures, while asecond operator may select J=25 for vacuum generators and for lightfixtures.

An operator selects to use the associated value for the number of flighthours of cycles from G % or J % based on which value corresponds to thegreater number of hours or cycles. For example, comparing the results ofthe lookup function L(G) and L(J), the operator selects the greatervalue. As data are collected over time, the lookup function will returndifferent values, and whether L(G) or L(J) is used as a the activethreshold to determine when to generate a service alert may changeaccordingly.

In some aspects, when the size of the data set against which the lookupfunction L(x) is run is below a predefined number of samples, the valueof L(x) may be replaced by a preset value of hours or cycles until apredetermined number of samples have been collected, and transition tothe lookup function L(G∥J) as more samples are collected. In oneexample, until fifty (50) removals have been observed, the activeremoval threshold may be set to 20,000 cycles (e.g., based on amanufacturer warranty, a threshold for prior version of the part, atesting regime, etc.), and after fifty removals have been observed, theactive removal threshold may use the number of hours/cycles indicatedaccording to the target removal threshold 310 or the survival threshold320.

${{Active}\mspace{14mu} {removal}\mspace{14mu} {threshold}} = \left\{ \begin{matrix}{{{preset}\mspace{14mu} {amount}\mspace{14mu} s} \leq {setpoint}} \\{{{L\left( {G{}J} \right)}s} > {setpoint}}\end{matrix} \right.$

In some aspects, the part life prediction control unit 106 compares theintersection 340 of the survival threshold 320 and the reliability curve300 against an upper reliability threshold 350 and a lower reliabilitythreshold 360 for the part, and generates a quality alert when thenumber of hours or cycles at the intersection 340 falls outside of thereliability thresholds. The quality alert may be transmitted to anaircraft operator, maintenance personnel, engineering team, supplier, orthe like to redesign the part or reset procedures for when a part isremoved. For example, when the intersection 340 correlates to a portionof the reliability curve 300 where the part is indicated to have areliability probability above the upper reliability threshold 350 (e.g.,80% reliable versus an upper reliability threshold 350 of 50%), the partlife prediction control unit 106 may generate an under-use alert toindicate that maintenance personnel are removing the part too frequentlyor too early. In another example, when the intersection 340 correlatesto a portion of the reliability curve 300 where the part is indicated tohave a reliability probability below the lower reliability threshold 360(e.g., 20% reliable versus a lower reliability threshold 360 of 30%),the part life prediction control unit 106 may generate an over-use alertto indicate that maintenance personnel are not removing the part earlyenough or frequently enough to avoid unplanned maintenance.

The part life prediction control unit 106 generates a service alert inresponse to the number of cycles or hours of flight (e.g., CSI/TSI)exceeding the active removal threshold (i.e., the selected targetremoval threshold 310 or the survival threshold 320). However, toreplace the part removed from the aircraft, a replacement part isneeded. Accordingly, the part life prediction control unit 106determines a forecasted replacement threshold 330 (that correlates to areorder threshold 430, discussed in relation to FIG. 5 to determine whento place an order for a part to be ready for installation when theoperator removes a given part (e.g., in response to the service alert)).An inventory order may be placed to a supplier to manufacture the part,a warehouse to deliver an existing part to a new location, etc. Theforecasted replacement threshold 330 represents an expected amount ofcycles or flight hours in advance of the removal threshold an operatorwould need to place an order for additional parts if the parts to beremoved are not held in inventory.

To determine the forecasted replacement threshold 330, the part lifeprediction control unit 106 looks up the lead time T_(Lead) of the part(i.e., the expected time from placing an order for a part to receivingthe part at a specified destination) from the parts database 102 andconverts the lead time to operational time based on data from thevehicle usage database 114 for the aircraft on which the part is to beinstalled. For example, the vehicle usage database 114 may include anexpected use rate for a given aircraft of z_(TSI) flight hours per timeperiod (e.g., z hours in flight per day/week/month/etc.) or z_(CSI)flights per time period (e.g., z flights per day/week/month/etc.), whichis converted to a number of cycles based on a historic cycles per flightcoefficient q for the part (e.g., q cycles of that part per flight; q=1when one flight corresponds to one cycle).

The part life prediction control unit 106 identifies the hours/cyclesspecified by the active removal threshold and calculates where to placethe forecasted replacement threshold 330 based on the expected use rateand the lead time, as indicated below.

${IL}_{lead} = \left\{ \begin{matrix}{{{Current}\mspace{14mu} {inventory}\mspace{14mu} {level}} - \ {\sum_{i = 1}^{N}{I_{P_{i}}\ {TSI}}}} \\{{{Current}\mspace{14mu} {inventory}\mspace{14mu} {level}}\  - \ {\sum_{i = 1}^{M}{I_{P_{i}}{CSI}}}}\end{matrix} \right.$

-   -   IF IL_(lead)<=Safety Stock, THEN Reorder Threshold=current        inventory level        -   ELSE No Reorder alert            Where N and M are the number of parts, which are associated            with either flight hours or flight cycles, available to the            operator, and

$I_{P_{i}} = \left\{ {{\begin{matrix}{0,} & {{if}\mspace{14mu} {the}\mspace{14mu} {hours}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {part}\mspace{14mu} {do}\mspace{14mu} {not}\mspace{14mu} {reach}\mspace{14mu} {the}\mspace{14mu} {threshold}\mspace{14mu} {during}\mspace{14mu} T_{Lead}} \\{1,} & {{if}\mspace{14mu} {the}\mspace{14mu} {hours}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {part}\mspace{14mu} {reach}\mspace{14mu} {the}\mspace{14mu} {threshold}\mspace{14mu} {during}\mspace{14mu} T_{Lead}}\end{matrix}{or}I_{P_{i}}} = \left\{ \begin{matrix}{0,} & {{if}\mspace{14mu} {the}\mspace{14mu} {cycles}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {part}\mspace{14mu} {do}\mspace{14mu} {not}\mspace{14mu} {reach}\mspace{14mu} {the}\mspace{14mu} {threshold}\mspace{14mu} {during}\mspace{14mu} T_{Lead}} \\{1,} & {{if}\mspace{14mu} {the}\mspace{14mu} {cycles}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {part}\mspace{14mu} {reach}\mspace{14mu} {the}\mspace{14mu} {threshold}\mspace{14mu} {during}\mspace{14mu} T_{Lead}}\end{matrix} \right.} \right.$

For example, for a part replaced based on TSI having a removal thresholdcorresponding to 20,000 flight hours, a lead time T_(Lead) of 90 days,and an expected use rate z_(TSI) of 100 hours per week, the part lifeprediction control unit 106 sets the forecasted replacement threshold330 to 19,825 flight hours (when rounded down to the nearest multiple of25 hours). In another example, for another part on the same aircraft asthe above example that is replaced based on CSI having a removalthreshold corresponding to 2,000 cycles, a lead time T_(Lead) of 100days, an expected use rate z_(CSI) of 20 flights per week, and 2 cyclesper flight, the part life prediction control unit 106 sets theforecasted replacement threshold 330 to 1,420 cycles (when rounded downto the nearest multiple of 10 cycles).

FIG. 5 illustrates an inventory curve 400 that tracks the number ofindividual parts (held by an operator or at a specific location by anoperator) over time. In some aspects, the inventory level 410 decreasesover time as parts are taken from inventory and placed into aircraft orother assemblies, and may increase (e.g., by a reorder quantity 440)when inventory is delivered or re-shelved (e.g., as in rotable partscleared for further use). By determining the lead time, T_(Lead), asdescribed herein, an operator may place an order to a supplied orupstream holding facility an expected amount of time before a safetystock level 420 is reached. When multiple values for T_(Lead) exist, forexample, when several curves are developed for a part in different usecases, the operator may use an average value for T_(Lead) or a smallestvalue for T_(Lead). In various aspects, the safety stock level 420 maybe zero or any quantity greater than zero.

The inventory level 410 defines a reorder threshold 430 at a timeT_(Lead) in advance of the time when the safety stock level 420 isexpected to be reached based on operational data received from users(e.g., of aircraft). For example, when a consumption rate is one partper day, the safety stock level 420 is fifty parts, and T_(Lead) is fivedays, the reorder threshold 430 is fifty-five parts. When the inventorylevel 410 reaches a reorder threshold 430, the part life predictioncontrol unit 106 generates and transmits an inventory alert to the partsdatabase 102. The parts database 102, in turn, determines whether toplace an order for the part to a supplier based on a number of that part(or an equivalent substitute part) currently held in inventory, minimumorder sizes for the part, minimum and maximum inventory carrying levelsfor that part, whether an alternative version of that part is available(e.g., a directive to use version 1.0 of part A or version 2.0 of part Ainterchangeably), whether a substitute part is available, etc.

FIG. 6 is a flowchart of a method 600 for generating service andinventory alerts for aircraft parts based on aircraft operations.

Method 600 begins with block 610, where a part reliability determinationsystem 100 generates a reliability curve 300 for an aircraft part (e.g.,according to method 200 discussed in relation to FIG. 3). Whengenerating the reliability curve 300, the part reliability determinationsystem collects usage data from some or all instances of the aircraftpart from an associated aircraft. The part reliability determinationsystem also collects usage data from several instances of the aircraftpart while these instances are still installed on aircraft. The usagedata indicate whether the given instance of the part is in a faultedstate at the time of removal and one or more of a number of cyclesaccumulated by a given instance of the aircraft part or a number offlight hours accumulated by a given instance of the aircraft part at thetime of removal from the associated aircraft 10. Based on the usage datafor the removed part, the part reliability determination system 100determines what percentage of the aircraft parts are in a faulted stateat several points of the reliability curve 300 (e.g., at a given numbersof cycles or at a given number of flight hours), and fits a probabilitydistribution function to the usage data indicating what the percentagesthe aircraft part in the faulted state at a plurality of numbers ofcycles or flight hours over the lifetime of the aircraft part. Invarious aspects, the aircraft parts that are removed in a non-faultedstate may be re-installed to the original aircraft or a differentaircraft to accumulate additional cycles and/or flight hours.

At block 620, the part reliability determination system 100 sets theactive removal threshold for the aircraft part on the reliability curve300. In one example, the part reliability determination system 100 setsthe active removal threshold based on the number of historic removals ofthe part used to generate the reliability curve 300 per block 610. Whenthe number of historic removals is below a predetermined amount, thepart reliability determination system 100 sets the active removalthreshold based on a preset value of number of cycles or flight hours(e.g., based on a manufacturer's recommendation). When the number offlight hours or cycles of historic removals exceeds a predeterminedamount, the part reliability determination system 100 sets the activeremoval threshold to one of the target removal threshold 310 or thesurvival threshold 320; whichever is associated with the greater numberof flight hours or cycles. In various aspects, as more samples ofremovals of the aircraft part are received from operators, the value ofthe active removal threshold for the aircraft part on the reliabilitycurve 300 may be reset based on the new and/or expanded removal data.

At block 630, the part reliability determination system 100 (optionally)sets the reorder threshold 430 for the aircraft part based on theremoval threshold. The reorder threshold 430 is determined to be at anearlier point in the lifecycle of the aircraft part to account for leadtimes (e.g., T_(Lead)) and the expected use rate of the aircraft and thepart in the aircraft. In aspects where a part type is tracked based onCSI, the part reliability determination system 100 sets the reorderthreshold 430 based on the removal threshold, a lead time for theaircraft part, a use rate of the aircraft 10, and a cycles-per-flightcoefficient for the aircraft part. In aspects where a part type istracked based on TSI, the part reliability determination system 100 setsthe reorder threshold 430 based on the removal threshold, a lead timefor the aircraft part, and a use rate of the aircraft. In variousaspects, the reorder threshold 430 is set based on Just In Time (JIT)order levels so that the inventory level 410 is equal to the safetystock level 420 when the ordered aircraft parts arrive.

At block 640, the part reliability determination system 100 tracksinstallation data for when installations of the aircraft part into anaircraft occurred. In various aspects, the installation of a given partinto a given aircraft is tracked according to a serial number or otheridentifier of the individual part associated with the given aircraft sothat at block 650, the part reliability determination system 100 cantrack operations of the aircraft part based on operations of theaircraft in which the aircraft part is installed. For example, when theaircraft is tracked as being in flight for 2,000 hours since the time ofinstallation of a given part, the part reliability determination system100 associates the installed part as having accumulated 2,000 flighthours. In aspects using rotable parts (i.e., parts that may beinstalled, uninstalled, and then reinstalled on the same or a differentaircraft), the part reliability determination system may add the time orcycles accumulated by a part in a current installation to the time orcycles accumulated in prior installations.

At block 660, the part reliability determination system 100 determineswhether the installation data and the operations of the aircraftindicate that a part installed thereon has satisfied the removalthreshold set at block 620. In response to determining that theoperations of the aircraft part satisfy the removal threshold, method600 proceeds to block 670. Otherwise, method 600 proceeds to block 680.

At block 670, the part reliability determination system 100 generates aservice alert. The part reliability determination system 100 maytransmit the service alert to aircraft operators, maintenance personnel,and the like to schedule maintenance for the aircraft. The service alertmay be transmitted over wired or wireless means as an electronic messageincluding text, a part identifier, an aircraft identifier, a date atwhich the service alert is generated, and other information related toservicing the aircraft to replace the aircraft part for which theservice alert is generated. In various aspects, the service alert isgenerated by the aircraft on which the aircraft part is currentlyinstalled in response to flight operations of that aircraft (e.g., onlanding, the service alert is generated if the removal threshold issatisfied). In other aspects, an operator system (e.g., a flightscheduling system) generates the service alert when a given aircraft isscheduled to satisfy the removal threshold or has reported operationsthat satisfy the removal threshold.

At block 680, the part reliability determination system 100 determineswhether the installation data and the operations of the aircraftindicate that a part installed thereon has satisfied the reorderthreshold 430. In response to determining that the operations of theaircraft part satisfy the reorder threshold 430, method 600 proceeds toblock 690. Otherwise, method 600 returns to block 640 to continuetracking the installation of parts and the operations of the aircraftand parts installed therein.

At block 690, the part reliability determination system 100 generates aninventory alert. The part reliability determination system 100 maytransmit the inventory alert to a parts database 102, a supplier ordersystem, a maintenance team, or the like. The inventory alert may betransmitted over wired or wireless means as an electronic messageincluding text, a part identifier, an aircraft identifier, a date atwhich the inventory alert is generated, a date at which an associatedservice alert is expected to be generated (based on currently projectedaircraft use rates), and other information related to ordering a partfor servicing the aircraft to replace the aircraft part for which theinventory alert is generated. In various aspects, the inventory alert isgenerated by the aircraft on which the aircraft part is currentlyinstalled in response to flight operations of that aircraft (e.g., onlanding, the inventory alert is generated if the inventory threshold issatisfied). In other aspects, an operator system (e.g., a flightscheduling system) generates the inventory alert when a given aircraftis scheduled to satisfy the inventory threshold or has reportedoperations that satisfy the inventory threshold. Method 600 then returnsto block 640 to continue tracking the installation of parts and theoperations of the aircraft and parts installed therein.

In the current disclosure, reference is made to various aspects.However, it should be understood that the present disclosure is notlimited to specific described aspects. Instead, any combination of thefollowing features and elements, whether related to different aspects ornot, is contemplated to implement and practice the teachings providedherein. Additionally, when elements of the aspects are described in theform of “at least one of A and B,” it will be understood that aspectsincluding element A exclusively, including element B exclusively, andincluding element A and B are each contemplated. Furthermore, althoughsome aspects may achieve advantages over other possible solutions and/orover the prior art, whether or not a particular advantage is achieved bya given aspect is not limiting of the present disclosure. Thus, theaspects, features, aspects and advantages disclosed herein are merelyillustrative and are not considered elements or limitations of theappended claims except where explicitly recited in a claim(s). Likewise,reference to “the invention” shall not be construed as a generalizationof any inventive subject matter disclosed herein and shall not beconsidered to be an element or limitation of the appended claims exceptwhere explicitly recited in a claim(s).

As will be appreciated by one skilled in the art, aspects describedherein may be embodied as a system, method or computer program product.Accordingly, aspects may take the form of an entirely hardware aspect,an entirely software aspect (including firmware, resident software,micro-code, etc.) or an aspect combining software and hardware aspectsthat may all generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects described herein may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatuses(systems), and computer program products according to aspects of thepresent disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the block(s) of the flowchart illustrationsand/or block diagrams.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other device to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the block(s) of the flowchartillustrations and/or block diagrams.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other device to cause aseries of operational steps to be performed on the computer, otherprogrammable apparatus or other device to produce a computer implementedprocess such that the instructions which execute on the computer, otherprogrammable data processing apparatus, or other device provideprocesses for implementing the functions/acts specified in the block(s)of the flowchart illustrations and/or block diagrams.

The flowchart illustrations and block diagrams in the Figures illustratethe architecture, functionality, and operation of possibleimplementations of systems, methods, and computer program productsaccording to various aspects of the present disclosure. In this regard,each block in the flowchart illustrations or block diagrams mayrepresent a module, segment, or portion of code, which comprises one ormore executable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order or out of order, dependingupon the functionality involved. It will also be noted that each blockof the block diagrams and/or flowchart illustrations, and combinationsof blocks in the block diagrams and/or flowchart illustrations, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

While the foregoing is directed to aspects of the present disclosure,other and further aspects of the disclosure may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

What is claimed is:
 1. A method, comprising: generating a reliability curve for a vehicle part by: collecting usage data regarding a plurality of instances of the vehicle part, wherein for each respective instance of the vehicle part of the plurality of instances of the vehicle part: the usage data for the respective instance are collected at a time of removal from a vehicle associated with the respective instance, the usage data indicate a number of cycles accumulated by the respective instance of the vehicle part at the time of removal from the associated vehicle, and the usage data indicate whether the respective instance of the vehicle part is in a faulted state at the time of removal from the associated vehicle; determining a percentage of instances of the vehicle part that are in the faulted state at a plurality of numbers of cycles; and fitting a probability distribution function that defines the reliability curve to the usage data based on the percentage of instances of the vehicle part that are in the faulted state for each of the plurality of numbers of cycles; setting a removal threshold on the reliability curve; tracking an installation of a given instance of the vehicle part into a given vehicle; tracking a number of cycles of the given instance of the vehicle part based on operations of the given vehicle in which the given instance of the vehicle part is installed; and in response to the number of cycles of the given instance of the vehicle part satisfying the removal threshold, transmitting a service alert to an operator of the given vehicle.
 2. The method of claim 1, wherein setting the removal threshold on the reliability curve further comprises: determining a number of historic removals of the vehicle part used to generate the reliability curve; and in response to the number of historic removals being below a predetermined setpoint, setting the removal threshold as a fixed number of cycles of the vehicle part.
 3. The method of claim 1, wherein setting the removal threshold on the reliability curve further comprises: setting the removal threshold between a first point and a second point on the reliability curve, wherein the first point indicates a first number of cycles of the vehicle part at which at least a first percentage of operators historically removed the vehicle part from vehicle, wherein the second point indicates a second number of cycles of the vehicle part that was previously set for the removal threshold for the vehicle part, and setting a larger of the first point and the second point as the removal threshold.
 4. The method of claim 1, further comprising: setting an reorder threshold based on: a safety stock level the removal threshold; a lead time for the vehicle part; a use rate of the given vehicle; and in response to the number of cycles of the given instance of the vehicle part satisfying the reorder threshold, transmitting an inventory alert to the operator of the given vehicle.
 5. The method of claim 1, further comprising: determining an intersection of the reliability curve and the removal threshold; comparing the intersection against an upper reliability threshold for the vehicle part; and in response to the number of cycles at the intersection being greater than the upper reliability threshold, transmitting a quality alert to the operator of the given vehicle.
 6. The method of claim 1, further comprising: determining an intersection of the reliability curve and the removal threshold; comparing the intersection against an a lower reliability threshold for the vehicle part; and in response to the number of cycles at the intersection being lower than the lower reliability threshold, transmitting a quality alert to the operator of the given vehicle.
 7. The method of claim 1, further comprising: in response to receiving additional samples of removals of the vehicle part, re-setting the removal threshold to a different value on the reliability curve.
 8. The method of claim 1, wherein the given vehicle is an aircraft.
 9. A method, comprising: generating a reliability curve for a vehicle part by: collecting usage data regarding a plurality of instances of the vehicle part, wherein for each respective instance of the vehicle part of the plurality of instances of the vehicle part: the usage data for the respective instance are collected at a time of removal from a vehicle associated with the respective instance, the usage data indicate a number of flight hours accumulated by the respective instance of the vehicle part at the time of removal from the associated vehicle, and the usage data indicate whether the respective instance of the vehicle part is in a faulted state at the time of removal from the associated vehicle; determining a percentage of instances of the vehicle part that are in the faulted state at a plurality of numbers of flight hours; and fitting a probability distribution function that defines the reliability curve to the usage data based on the percentage of instances of the vehicle part that are in the faulted state for each of the plurality of numbers of flight hours; setting a removal threshold on the reliability curve; tracking an installation of a given instance of the vehicle part into a given vehicle; tracking a number of flight hours of the given instance of the vehicle part based on operations of the given vehicle in which the given instance of the vehicle part is installed; and in response to the number of flight hours of the given instance of the vehicle part satisfying the removal threshold, transmitting a service alert to an operator of the given vehicle.
 10. The method of claim 9, wherein setting the removal threshold further comprises: determining a number of historic removals of the vehicle part used to generate the reliability curve; and in response to the number of historic removals being below a predetermined setpoint, setting the removal threshold as a fixed value of flight hours for the vehicle part.
 11. The method of claim 9, wherein setting the removal threshold further comprises: setting the removal threshold between a first point and a second point on the reliability curve, wherein the first point indicates a first number of flight hours accumulated by the vehicle part at which at least a first percentage of operators historically removed the vehicle part from vehicle, and wherein the second point indicates a second number of flight hours accumulated by the vehicle part that was previously set as the removal threshold for the vehicle part, and wherein a larger of the first point and the second point is set as the removal threshold.
 12. The method of claim 9, further comprising: setting a reorder threshold based on: a safety stock level; the removal threshold; a lead time for the vehicle part; and a use rate of the given vehicle; and in response to the number of flight hours accumulated by the given instance of the vehicle part satisfying the reorder threshold, transmitting an inventory alert to the operator of the given vehicle.
 13. The method of claim 9, further comprising: determining an intersection of the reliability curve and the removal threshold; comparing the intersection against an upper reliability threshold for the vehicle part; and in response to a number of cycles at the intersection being greater than the upper reliability threshold, transmitting a quality alert to an operator of the given vehicle.
 14. The method of claim 9, further comprising: determining an intersection of the reliability curve and the removal threshold; comparing the intersection against a lower reliability threshold for the vehicle part; and in response to a number of cycles at the intersection being lower than the lower reliability threshold, transmitting a quality alert to the operator of the given vehicle.
 15. The method of claim 9, further comprising: in response to receiving additional samples of removals of the vehicle part, re-setting the removal threshold to a different value on the reliability curve.
 16. The method of claim 9, wherein the given vehicle is an aircraft.
 17. A system, comprising: a processor; and a memory storage device, including instructions that when performed by the processor enable the processor to perform an operation comprising: generating a reliability curve for a vehicle part; setting a removal threshold on the reliability curve; tracking an installation of a given instance of the vehicle part into a given vehicle; tracking operations of the given instance of the vehicle part based on operations of the given vehicle in which the given instance of the vehicle part is installed; and in response to the operations of the given instance of the vehicle part satisfying the removal threshold, transmitting a service alert to the operator of the given vehicle.
 18. The system of claim 17, wherein setting the removal threshold further comprises: determining a number of historic removals of the vehicle part used to generate the reliability curve; and in response to the number of historic removals being below a predetermined setpoint, setting the removal threshold as a fixed value of operations of the given vehicle.
 19. The system of claim 17, wherein setting the removal threshold further comprises: determining a number of historic removals of the vehicle part used to generate the reliability curve; and in response to the number of historic removals exceeding a predetermined setpoint: setting the removal threshold between a first point and a second point on the reliability curve, wherein the first point indicates a first number of operations of the vehicle part at which at least a first percentage of operators historically removed the vehicle part from vehicle, and wherein the second point indicates a second number of operations of the vehicle part at which at least a second percentage of operators historically removed the vehicle part from the vehicle that is greater than the first percentage.
 20. The system of claim 17, wherein generating the reliability curve for the vehicle part further comprises: collecting usage data from several instances of the vehicle part at a time of removal from an associated vehicle that indicate a number of cycles accumulated by a particular instance of the vehicle part at the time of removal from the associated vehicle and whether the particular instance of the vehicle part is in a faulted state at the time of removal; determining percentages the vehicle part in the faulted state per number of cycles; and fitting a probability distribution function to the usage data based on the percentages of the vehicle part in the faulted state.
 21. The system of claim 17, wherein generating the reliability curve for the vehicle part further comprises: collecting usage data from several instances of the vehicle part at a time of removal from an associated vehicle that indicate a number of flight hours accumulated by a particular instance of the vehicle part at the time of removal from the associated vehicle and whether the particular instance of the vehicle part is in a faulted state at the time of removal; determining percentages the vehicle part in a faulted state per hours of flight; and fitting a probability distribution function to the usage data based on the percentages of the vehicle part in the faulted state.
 22. The system of claim 17, wherein the operations of the vehicle part are tracked based on a number of cycles of the vehicle part while the vehicle part is installed, further comprising: setting a reorder threshold based on: the removal threshold; a lead time for the vehicle part; a use rate of the given vehicle; and a cycles per flight coefficient for the vehicle part; and in response to the operations of the given instance of the vehicle part satisfying the reorder threshold, transmitting an inventory alert to an operator of the given vehicle.
 23. The system of claim 17, wherein the operations of the vehicle part are tracked based on a number of flight hours of the given vehicle while the vehicle part is installed, further comprising: setting a reorder threshold based on: the removal threshold; a lead time for the vehicle part; and a use rate of the given vehicle; and in response to the operations of the given instance of the vehicle part satisfying the reorder threshold, transmitting an inventory alert to an operator of the given vehicle.
 24. The system of claim 17, wherein the operation further comprises: determining an intersection of the reliability curve and the removal threshold; comparing the intersection against an upper reliability threshold and a lower reliability threshold for the vehicle part; and in response to a number of operations of the vehicle part at the intersection being one of greater than of the upper reliability threshold or less than the lower reliability threshold, transmitting a quality alert to an operator of the given vehicle.
 25. The system of claim 17, wherein the given vehicle is an aircraft. 